Nonparametric Discriminant Analysis for Face Recognition

In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussia...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 31; H. 4; S. 755 - 761
Hauptverfasser:	Li, Zhifeng, Lin, Dahua, Tang, Xiaoou
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.04.2009 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Artificial intelligence Boolean functions Classifier design and evaluation Computer science; control theory; systems Data structures Degradation Discriminant Analysis Exact sciences and technology Face Face and gesture recognition Face recognition Gaussian distribution Humans Image databases Linear discriminant analysis Mathematical analysis Matrices Matrix methods Null space Pattern Recognition, Automated - statistics & numerical data Pattern recognition. Digital image processing. Computational geometry Scatter Scattering Spatial databases Statistics, Nonparametric Studies Gabor filter Discriminant analysis Image recognition Face recognition Image processing classifier design and evaluation Image databank Non gaussian noise Gaussian distribution Subspace method Autoregressive model Regression analysis Pattern recognition multiclassifier fusion nonparametric Classification Facies Database Pattern analysis Artificial intelligence Multiclass discriminant analysis (NDA)
ISSN:	0162-8828, 1939-3539
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Abstract	In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database.
AbstractList	In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric [abstract truncated by publisher]. In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases.
Author	Dahua Lin Zhifeng Li Xiaoou Tang
Author_xml	– sequence: 1 givenname: Zhifeng surname: Li fullname: Li, Zhifeng email: zfli@se.cuhk.edu.hk organization: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong. zfli@se.cuhk.edu.hk – sequence: 2 givenname: Dahua surname: Lin fullname: Lin, Dahua – sequence: 3 givenname: Xiaoou surname: Tang fullname: Tang, Xiaoou
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Keywords	Gabor filter Discriminant analysis Image recognition Face recognition Image processing classifier design and evaluation Image databank Non gaussian noise Gaussian distribution Subspace method Autoregressive model Regression analysis Pattern recognition multiclassifier fusion nonparametric Classification Facies Database Pattern analysis Artificial intelligence Multiclass discriminant analysis (NDA)
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SubjectTerms	Algorithms Applied sciences Artificial intelligence Boolean functions Classifier design and evaluation Computer science; control theory; systems Data structures Degradation Discriminant Analysis Exact sciences and technology Face Face and gesture recognition Face recognition Gaussian distribution Humans Image databases Linear discriminant analysis Mathematical analysis Matrices Matrix methods Null space Pattern Recognition, Automated - statistics & numerical data Pattern recognition. Digital image processing. Computational geometry Scatter Scattering Spatial databases Statistics, Nonparametric Studies
Title	Nonparametric Discriminant Analysis for Face Recognition
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